1. Field of the Invention
The present invention is directed to queue monitoring and prediction systems and is more specifically related to the tracking and predictions of queue length using crowd sourced smart phones.
2. Description of the Related Art
While there are systems to gather and present information regarding traffic congestion, for instance the Waze app or the traffic feature built into Apple Maps, there is no similar system that collects information about the queues that form in many public venues.
As a result, a couple might rush through dinner to get to a movie theatre thinking there would be a line to get in, only to find the theatre had very light traffic that night. Or a driver might pull off the highway to get a cup of coffee only to find that there was a five minute line inside the shop and a similar one in the drive through line—yet unbeknownst to the driver the coffee shop on the other corner had no line. Or a shopper might move across the grocery store to check out only to find a long line—a fact that if known in advance might have allowed that person to shop for some other items.
Queues form all the time at public venues. Traffic at public venues is variable and unpredictable in the short run and sometimes the long run as well. As a result, staffing and facilities are deployed so as to meet a level of demand below peak demand (as it would be too expensive to staff for peak demand) and a result queues form from time to time. At some level, the establishment balances the cost of extra staffing against the expressed pain customers feel from standing in line.
Queues, however, do come and go, for instance at a popular coffee shop, where there might be seven people in line in one minute and five minutes later, none. When queues form customers either balk (leave the line) or wait in line. Perhaps more staffing is deployed. But eventually, queues dissipate and over longer periods of time, “supply” (e.g. cashiers, attendants) matches “demand” (customers needing service).
The interests of service providers and consumers, from an informational point of view, are not always aligned. Businesses might wish to not broadcast the presence of queues (so as not to discourage customers), while consumers would very much like to know about the existence of current queues, or the probability of a one forming, in the time it would take them to get to the establishment.
Once a consumer has made an investment in consuming a service (researching the service, driving to the establishment, selecting items) it becomes harder to balk at a line. Ideally, the consumer would like to know about the probability of a queue coinciding with their consumption of the service before they start to make those investments. It may be beneficial if the consumer somehow compensated for having to wait in line.
Some decisions are made strictly based on queue length at that moment. For instance, if one is at the back of a grocery store, it might be useful to know how long the lines are at the checkout counter right then and there. If they are long, one might shop for a few more items but if they are short, it might be best to hustle up and catch a cashier while the lines are short. In this case, a decision is largely made on the queue at that very moment.
Currently, there are systems, such as described in Yan Wang, et al's paper “Tracking Human Queues Using Single-Point Signal Monitoring”, that employ the use of iBeacons at cash registers. These systems can detect smart phones nearby and assemble that data into queue information. The drawback to this system is that the information is collected and owned by the establishment. There is no method to distribute it to the public and no incentive to do so. Furthermore, it is not a universal system with queuing data available for all establishments—just those that install iBeacons.
Another system, described in “LineKing: Crowdsourced Line Wait-Time Estimation using Smartphones” by Muhammed Fatih Bulut, et al, requires users to install an app, which then looks for the presence of Wi-Fi signals associated with a specific venue. Upon sensing such signals, the system assumes the user has arrived at the venue then tracks the total time that the signal can be received. For each visit to a venue, the system tracks the total time spent in range of the Wi-Fi signal. This time is assumed to be the combined queue and service time.
The drawbacks to this system are that it cannot separate out the waiting time from the service time. So a ten minute to get a table cannot be separated from the hour spent at that table. The system also does not report any data until the app user has transited in and out of the venue. That is not helpful to someone who needs to make a decision moments after the app-user enters the queue.
A problem related to queuing is one of crowds in general. For example, a family might wish to go to the beach on a nice day but if the beach is too crowded they might. wish to go somewhere else. If a restaurant is full but still has no queue per se, a customer may wish to go somewhere else due to the anticipated noise or slow service. Or a crowded movie theatre might be considered undesirable and thus something to avoid if a person could find out before buying a ticket. Ski lift lines are another location where queue length is important to the skier.
On the other hand, a crowd might be an attractive attribute at a different location and different time. For instance, it might be that one bar or nightclub might be preferable to another if the crowd was larger. Similarly, it might be useful to know if a certain pep rally or other public social functions had drawn enough of a crowd to be worth attending. Furthermore, it would be useful, if invited to a private party, whether a certain number of people appeared to be there.
This invention creates an information system to present consumers with real and predicted data about queue behavior at a localized level. Using a localized level allows consumers to make alternative plans and new decisions before they are faced with having to get in line and study the queue behavior themselves. It further serves to characterize crowds in the same way thus allowing users the opportunity to avoid crowds or seek them out, as the case may be.
The invention allows for the real-time collection of queuing information, the analysis of such information in order to predict future queue behavior, and presenting such information to consumers in a simple, fast way that will allow to make new, incremental decisions. For businesses if offers an opportunity to be apprised of their queue behaviors and to compensate consumers for certain queue outcomes.